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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Design and Analysis of Convolutional Neural Network for RF Signal Modulation Classification for In-Orbit Deployment

To effectively transmit data to and from satellites requires a complex and robust RF communication system. Commonly, several different types of signal modulations may be required to maximize satellite efficiency depending on a variety of unexpected channel impairments. We propose a neural network algorithm capable of learning these RF signal modulations using a supervised learning technique designed for low power, high-efficiency in-orbit deployment. The work presented demonstrates a convolutional neural network (CNN) capable of learning and recognizing a set of modulation schemes commonly used to transmit RF information. We are capable of recognizing the modulation scheme from the I and Q data channels directly, with no preprocessing or data conversion required other than breaking the incoming signal into a set of uniform normalized samples. We perform a network design and size analysis, showing that reasonably high accuracy can be obtained using networks with a relatively low number of trainable parameters. Given that a user of a system such as this may wish to receive a signal using a modulation scheme that the network has not previously learned, we demonstrate that transfer learning can learn new modulation schemes by retraining only the fully connected layers in the CNN. Thus, this type of network would excel in outer space deployment using high-efficiency transfer learning hardware. Modulation recognition can be performed through rapid feedforward computation, and the CNN training process is significantly simplified when learning new modulations is required.

CNN↗
NASA NTRS2017

A Machine Learning Concept for DTN Routing

This paper discusses the concept and architecture of a machine learning based router for delay tolerant space networks. The techniques of reinforcement learning and Bayesian learning are used to supplement the routing decisions of the popular Contact Graph Routing algorithm. An introduction to the concepts of Contact Graph Routing, Q-routing and Naive Bayes classification are given. The development of an architecture for a cross-layer feedback framework for DTN (Delay-Tolerant Networking) protocols is discussed. Finally, initial simulation setup and results are given.

Delay Tolerant Networks↗
NASA NTRS2024

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Yang Zhang↗
NASA NTRSDate not supplied

Microservice Architecture for Cognitive Networks

This develops the concept of a cognitive network and describes a microservice based architecture which could be used to implement such a system. Delay tolerant networking (DTN) influences the design of the architecture as well as the networking scenarios that the system attempts to address. A cognitive storage and fragmentation service is developed based on existing artificial intelligence techniques such as Advantage Actor Critic (A2C) and Deep Q-Networks. The system is simulated using OpenAI Gym in a custom developed DTN environment.

cognitive networks↗
NASA NTRS2002

The Right Amount of Glue: Technologies and Standards Relevant to a Future Solar-Terrestrial Data Environment

In order to meet the challenge of developing a new system science, we will need to employ technology that enables researchers to access data from fields with which they are at least initially unfamiliar as well as from sources they use more regularly. At the same time, the quantity of data to be obtained by missions such as the Solar Dynamics Observatory demands ease and simplicity of data access. These competing demands must in turn fit within severely constrained funding for data analysis in such projects. Based on experience in only a single discipline but with a diversity of data types and sources, we will give examples of technology that have made a significant difference in the way people do science. Similarly, we will show how adoption of a well-documented data format has made it easier for one community to search, reduce, and analyze data. We will also describe a community-supported data reduction and analysis software tree with useful features. We will attempt to generalize the lessons learned in these instances to features the broader, solar-terrestrial community might find compelling, while avoiding overdesign of a common data environment.

Gurman, J. B.↗
NASA NTRS2005

Time-Accurate Computations of Isolated Circular Synthetic Jets in Crossflow

Results from unsteady Reynolds-averaged Navier-Stokes computations are described for two different synthetic jet flows issuing into a turbulent boundary layer crossflow through a circular orifice. In one case the jet effect is mostly contained within the boundary layer, while in the other case the jet effect extends beyond the boundary layer edge. Both cases have momentum flux ratios less than 2. Several numerical parameters are investigated, and some lessons learned regarding the CFD methods for computing these types of flow fields are outlined. Results in both cases are compared to experiment.

Rumsey, Christoper L.↗
NASA NTRS2007

Time-Accurate Computations of Isolated Circular Synthetic Jets in Crossflow

Results from unsteady Reynolds-averaged Navier-Stokes computations are described for two different synthetic jet flows issuing into a turbulent boundary layer crossflow through a circular orifice. In one case the jet effect is mostly contained within the boundary layer, while in the other case the jet effect extends beyond the boundary layer edge. Both cases have momentum flux ratios less than 2. Several numerical parameters are investigated, and some lessons learned regarding the CFD methods for computing these types of flow fields are summarized. Results in both cases are compared to experiment.

Rumsey, C. L.↗
NASA NTRSDate not supplied

How To Train Your Dragon (and Vice Versa) – Human Spaceflight Research and The First Commercial Crews

The International Space Station has been an orbiting laboratory for human spaceflight research for over 20 years. NASA’s human research implementation teams have worked to develop, refine, and execute pre-, in-, and post-flight sessions to collect critical research data that will enable future spaceflight missions. The teams have worked closely with the spaceflight vehicle teams, including those for Soyuz and the now retired Space Shuttle vehicles, to successfully conduct this research. As NASA has transitioned to commercial vehicles for transport of crews to the ISS, the implementation teams have expanded their scope to include the SpaceX and upcoming Boeing vehicles. This session will focus on the unique implementation needs for the first available commercial platform, SpaceX. We will identify implementation adjustments made to accommodate the SpaceX Dragon system, including crew training and preflight baseline data collection, inflight operations tied to landing, and postflight planning and crew testing, as well as offer lessons learned from the past three SpaceX long-duration crew missions. Representatives from the Flight Operations Directorate, including the Astronaut Office and the vehicle recovery team, will also provide their perspectives on this new system, and how it affects their work with the human research community. A panel discussion with dedicated time for Q&A will follow.

Pasha Morshedi↗
NASA NTRS2002

Electron-phonon interaction on optical spectra of nanoelectronic devices

Information obtained on the solid-state lattice dynamics by electron-phonon interaction between lattice phonons and electrons could open up to learn more about lattice dynamics and to apply it in nanoelectronic devices including software reliability, nano-size capacitors, master clock sources, as well as non-contact temperature probes on nano-electronic and photonicdevices.

electron-phonon interactions lattice dynamics SrTi↗
NASA NTRSDate not supplied

An Evening with NASA Scientists and Engineers: A Senior Living Community Outreach Program

Introduction: An Evening with NASA Scientists and Engineers is an outreach program for senior living communities, where participants learn from and interact with a scientist or engineer in a virtual setting. Participants learn about NASA science and engineering through a behind-the-scenes look from the people who work at NASA. The program focuses on both the stories behind the discoveries and the stories behind the people. The audience tunes in to the events via the NASA Zoom Webinar platform, where they have the opportunity to ask questions throughout the approximately 30-minute program. By bringing the audience into the conversation, we build rapport and thus improve their connection with NASA. Overview: An Evening with NASA Scientists and Engineers began in September 2020 when we identified a need for outreach to this particular demographic, as senior living communities had been hit especially hard by the COVID-19 pandemic. The program covers a wide range of topics, including heliophysics, astrophysics, planetary science, Earth science, planetary defense, and more, depending on what is topically appropriate at the time, and occurs on a roughly monthly basis. Each interactive presentation is between 20-25 minutes with 5-10 minutes of dedicated Q&A. Future dates and topics may change depending on new developments to NASA missions and stories. Audience: As of December 2021, we have four regularly attending senior living communities, spanning the East Coast from New Hampshire to Florida, with each joining the overall program at various stages of its development. The ability to reach many different senior living communities is crucial so that a diversity of people may benefit from this program. The audience consists of seniors with varying experience and comprehension levels – some are very familiar with the sciences while others are simply curious to learn more. Residents tune in to each event from their individual rooms or from their larger community rooms. RiverWoods Exeter. The RiverWoods Exeter community was our initial audience who acted as our “guinea pig” to see how this program might be implemented and to work out any major issues. They are located in Exeter, NH but also have two additional campuses in Durham, NH and Manchester, NH, that joined us a few months into the program. Culpepper Garden. Our second community to join the program was Culpepper Garden, a senior living community in Arlington, VA that provides both independent and assisted living housing to low-income seniors. Edinburgh Square. Our third community, Edinburgh Square, connected with us after hearing about the program through Culpepper Garden. Edinburgh Square is an HUD-subsidized retirement community in Roanoke, VA that houses both seniors and those living with disabilities. Moonstruck Astronomy Club. The Moonstruck Astronomy Club consists of residents from the On Top of the World retirement community in Ocala, FL and was the latest senior living community to join us. NASA Zoom Webinar: An Evening with NASA Scientists and Engineers reaches numerous senior living communities through live presentations that we host using the NASA Zoom Webinar virtual platform. Participants are typically already familiar with the Zoom platform which enables a lower barrier to entry. A recording of each talk is added to a designated video playlist on the NASA STEM YouTube channel so that audiences worldwide may view them [1]. These recordings began with our April 1, 2021 presentation, as this was when NASA Zoom Webinar was implemented, thus enabling us to record each talk. Metrics: We record attendance numbers from the senior living communities for each event as well as those from YouTube views after the presentation recording is posted online. Depending on which communities tune in, typical attendance numbers are between 60-100 people. RiverWoods Exeter has the most regularly attending residents, with an average of 55 attendees per event. As of December 2021, we have hosted fourteen events, nine of which were recorded and posted to our designated NASA STEM YouTube Channel playlist. The nine recordings combined have roughly 4,000 YouTube views thus far, with specific videos seeing higher view counts around launch dates (e.g. NASA’s DART mission) and trending topics (e.g. JWST). A list of speakers, topics, and links to presentation recordings is shown in Table 1. Future Program Expansion: Thus far, we have been adding communities on an individual basis. To make this process more streamlined, we are looking into working with senior living community networks and organizations such as LeadingAge [2]. In this manner, we will be able to reach more communities while also making it easier to send and receive relevant information.

S L Tiedeken↗
NASA NTRS2013

The Nature of C Asteroid Regolith from Meteorite Observations

Regolith from C (and related) asteroid bodies are a focus of the current missions Dawn at Ceres, Hayabusa 2 and OSIRIS REx. An asteroid as large as Ceres is expected to be covered by a mature regolith, and as Hayabusa demonstrated, flat and therefore engineeringly-safe ponded deposits will probably be the sampling sites for both Hayabusa 2 and OSIRIS REx. Here we examine what we have learned about the mineralogy of fine-grained asteroid regolith from recent meteorite studies and the examination of the samples harvested from asteroid Itokawa by Hayabusa.

Zolensky, M.↗
NASA NTRS2013

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Hurricanes↗
NASA NTRSDate not supplied

OSIRIS-REx Earth Return & Entry: Navigation Operations & Lessons Learned

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft successfully returned to Earth on September24, 2024, safely delivering its Sample Return Capsule (SRC) to the Utah Test and Training Range (UTTR). This paper describes the navigation operations that occurred between the departure from Bennu and the return of the SRC. An overview is given of the Flight Dynamics System (FDS) that includes tracking, orbit determination (OD), maneuver planning, and interfaces with entry, descent, and landing. Operational details on the SRC release criteria and conjunction assessment considerations are also provided. Lessons learned are presented that may help future sample return or interplanetary entry missions.

Kenneth M. Getzandanner↗
NASA NTRSDate not supplied

DIP for Service Providers

The second DIP workshop’s topic is DIP for Service Providers. The workshop presentation covers topics such as DIP onboarding process, service authentication, catalog service capabilities, API requirements, NASA services and their access points, and data requirements for service providers. The Onboarding Process describes the required documentation that enables to collaborate with NASA as well as other industry partners through DIP. Also, step by step approach for the partner to take to register services or consme services through the DIP platform. The Catalog Service describes the different ways for the users to search for services that are of interest as well as registering services to the platform, so other users can search and discover. The API Requirements and Service Specifications describes the benefits of DIP's API management approach via API Gateway where services are managed centrally but ownership is decentralized and partners completely own the administration of their own services. NASA Services and Data Access Points topic describes the services, both data access service and machine learning prediction service, that NASA provides via the DIP platform in near time and post operations via REST API endpoints defined on the API Gateway. Also, NASA's approach for data storage process is explained. The Data Requirements for service providers describes the requirements for storing and exposing to support the performance of services that are provided by the service providers. Lastly, NASA's technology development plan with preliminary scheduler will be presented. There will be a Q&A session at the end of each segment of the presentation to solicit feedback from the participants.

ATM-X↗
NASA NTRS2010

Ares I-X: First Step in a New Era of Exploration

Since 2005, NASA's Constellation Program has been designing, building, and testing the next generation of launch and space vehicles to carry humans beyond low-Earth orbit (LEO). On October 28, 2009, the Ares Projects successfully launched the first suborbital development flight test of the Ares I crew launch vehicle, Ares I-X, from Kennedy Space Center (KSC). Although the final Constellation Program architecture is under review, data and lessons obtained from Ares I-X can be applied to any launch vehicle. This presentation will discuss the mission background and future impacts of the flight. Ares I is designed to carry up to four astronauts to the International Space Station (ISS). It also can be used with the Ares V cargo launch vehicle for a variety of missions beyond LEO. The Ares I-X development flight test was conceived in 2006 to acquire early engineering, operations, and environment data during liftoff, ascent, and first stage recovery. Engineers are using the test flight data to improve the Ares I design before its critical design review the final review before manufacturing of the flight vehicle begins. The Ares I-X flight test vehicle incorporated a mix of flight and mockup hardware, reflecting a similar length and mass to the operational vehicle. It was powered by a four-segment SRB from the Space Shuttle inventory, and was modified to include a fifth, spacer segment that made the booster approximately the same size as the five-segment SRB. The Ares I-X flight closely approximated flight conditions the Ares I will experience through Mach 4.5, performing a first stage separation at an altitude of 125,000 feet and reaching a maximum dynamic pressure ("Max Q") of approximately 850 pounds per square foot. The Ares I-X Mission Management Office (MMO) was organized functionally to address all the major test elements, including: first stage, avionics, and roll control (Marshall Space Flight Center); upper stage simulator (Glenn Research Center); crew module/launch abort system simulator (Langley Research Center); and ground systems and operations (KSC). Interfaces between vehicle elements and vehicle-ground elements, as well as environment analyses were performed by a systems engineering and integration team at Langley. Experience and lessons learned from these integrated product teams area are already being integrated into the Ares Projects to support the next generation of exploration launch vehicles.

Davis, Stephan R.↗
NASA NTRS2005

Distributed Adaptive Control: Beyond Single-Instant, Discrete Variables

In extensive form noncooperative game theory, at each instant t, each agent i sets its state x, independently of the other agents, by sampling an associated distribution, q(sub i)(x(sub i)). The coupling between the agents arises in the joint evolution of those distributions. Distributed control problems can be cast the same way. In those problems the system designer sets aspects of the joint evolution of the distributions to try to optimize the goal for the overall system. Now information theory tells us what the separate q(sub i) of the agents are most likely to be if the system were to have a particular expected value of the objective function G(x(sub 1),x(sub 2), ...). So one can view the job of the system designer as speeding an iterative process. Each step of that process starts with a specified value of E(G), and the convergence of the q(sub i) to the most likely set of distributions consistent with that value. After this the target value for E(sub q)(G) is lowered, and then the process repeats. Previous work has elaborated many schemes for implementing this process when the underlying variables x(sub i) all have a finite number of possible values and G does not extend to multiple instants in time. That work also is based on a fixed mapping from agents to control devices, so that the the statistical independence of the agents' moves means independence of the device states. This paper also extends that work to relax all of these restrictions. This extends the applicability of that work to include continuous spaces and Reinforcement Learning. This paper also elaborates how some of that earlier work can be viewed as a first-principles justification of evolution-based search algorithms.

Wolpert, David H.↗
NASA NTRS2018

Artificial Neural Networks for Determining Magnetospheric Conditions

This chapter presents a neural-network-based technique that allows for the reconstruction of the global, time-varying distribution of some physical quantity Q, that has been sparsely sampled at various locations within the magnetosphere, and at different times. We begin with a general introduction to the problem of prediction and specification, and why it is important and difficult to achieve with existing methods. We then provide a basic introduction to neural networks, and describe our technique using the specific example of reconstructing the electron plasma density in the Earth's inner magnetosphere on the equatorial plane. We then show more advanced uses of the technique, including 3D reconstruction of the plasma density, specification of chorus and hiss waves, and energetic particle fluxes. We summarize and conclude with a general discussion of how machine learning techniques might be used to advance the state-of-the-art in space weather prediction, and insight discovery.

Bortnik, Jacob↗
NASA NTRS2016

James Webb Space Telescope Integrated Science Instrument Module Thermal Vacuum Thermal Balance Test Campaign at NASA's Goddard Space Flight Center

The James Webb Space Telescope is a large infrared telescope with a 6.5-meter primary mirror, designed as a successor to the Hubble Space Telescope when launched in 2018. Three of the four science instruments contained within the Integrated Science Instrument Module (ISIM) are passively cooled to their operational temperature range of 36K to 40K with radiators, and the fourth instrument is actively cooled to its operational temperature of approximately 6K. Thermal-vacuum testing of the flight science instruments at the ISIM element level has taken place in three separate highly challenging and extremely complex thermal tests within a gaseous helium-cooled shroud inside Goddard Space Flight Centers Space Environment Simulator. Special data acquisition software was developed for these tests to monitor over 1700 flight and test sensor measurements, track over 50 gradients, component rates, and temperature limits in real time against defined constraints and limitations, and guide the complex transition from ambient to final cryogenic temperatures and back. This extremely flexible system has proven highly successful in safeguarding the nearly $2B science payload during the 3.5-month-long thermal tests. Heat flow measurement instrumentation, or Q-meters, were also specially developed for these tests. These devices provide thermal boundaries o the flight hardware while measuring instrument heat loads up to 600 mW with an estimated uncertainty of 2 mW in test, enabling accurate thermal model correlation, hardware design validation, and workmanship verification. The high accuracy heat load measurements provided first evidence of a potentially serious hardware design issue that was subsequently corrected. This paper provides an overview of the ISIM-level thermal-vacuum tests and thermal objectives; explains the thermal test configuration and thermal balances; describes special measurement instrumentation and monitoring and control software; presents key test thermal results; lists problems encountered during testing and lessons learned.

JWST ISIM Thermal↗